Using deep learning to understand and mitigate the qubit noise environment

Understanding the spectrum of noise acting on a qubit can yield valuable information about its environment, and crucially underpins the optimization of dynamical decoupling protocols that can mitigate such noise. However, extracting accurate noise spectra from typical time-dynamics measurements on qubits is intractable using standard methods. Here, we propose to address this challenge using deep learning algorithms, leveraging the remarkable progress made in the field of image recognition, natural language processing, and more recently, structured data. We demonstrate a neural network based methodology that allows for extraction of the noise spectrum associated with any qubit surrounded by an arbitrary bath, with significantly greater accuracy than the current methods of choice. The technique requires only a two-pulse echo decay curve as input data and can further be extended either for constructing customized optimal dynamical decoupling protocols or for obtaining critical qubit attributes such as its proximity to the sample surface. Our results can be applied to a wide range of qubit platforms, and provide a framework for improving qubit performance with applications not only in quantum computing and nanoscale sensing but also in material characterization techniques such as magnetic resonance.Comment: Accepted for publication, 15 pages, 10 figure

A Search for Dark Matter Annihilation in Galaxy Groups

We use 413 weeks of publicly-available $\textit{Fermi}$ Pass 8 gamma-ray data, combined with recently-developed galaxy group catalogs, to search for evidence of dark matter annihilation in extragalactic halos. In our study, we use luminosity-based mass estimates and mass-to-concentration relations to infer the $J$-factors and associated uncertainties for hundreds of galaxy groups within a redshift range $z \lesssim 0.03$. We employ a conservative substructure boost-factor model, which only enhances the sensitivity by an $\mathcal{O}(1)$ factor. No significant evidence for dark matter annihilation is found and we exclude thermal relic cross sections for dark matter masses below $\sim$30 GeV to 95% confidence in the $b\bar{b}$ annihilation channel. These bounds are comparable to those from Milky Way dwarf spheroidal satellite galaxies. The results of our analysis increase the tension, but do not rule out, the dark matter interpretation of the Galactic Center excess. We provide a catalog of the galaxy groups used in this study and their inferred properties, which can be broadly applied to searches for extragalactic dark matter.Comment: 5+18 pages, 1+14 figures, catalog available at: https://github.com/bsafdi/DMCat; v2 updated to journal version with several updates, results and conclusions unchange

Towards Populating Generalizable Engineering Design Knowledge

Aiming to populate generalizable engineering design knowledge, we propose a method to extract facts of the form head entity :: relationship :: tail entity from sentences found in patent documents. These facts could be combined within and across patent documents to form knowledge graphs that serve as schemes for representing as well as storing design knowledge. Existing methods in engineering design literature often utilise a set of predefined relationships to populate triples that are statistical approximations rather than facts. In our method, we train a tagger to identify both entities and relationships from a sentence. Given a pair of entities thus identified, we train another tagger to identify the relationship tokens that specifically denote the relationship between the pair. For training these taggers, we manually construct a dataset of 44,227 sentences and corresponding facts. We also compare the performance of the method against typically recommended approaches, wherein, we predict the edges among tokens by pairing the tokens independently and as part of a graph. We apply our method to sentences found in patents related to fan systems and build a domain knowledge base. Upon providing an overview of the knowledge base, we search for solutions relevant to some key issues prevailing in fan systems. We organize the responses into knowledge graphs and hold a comparative discussion against the opinions from ChatGPT

Order and Disorder in AKLT Antiferromagnets in Three Dimensions

The models constructed by Affleck, Kennedy, Lieb, and Tasaki describe a family of quantum antiferromagnets on arbitrary lattices, where the local spin S is an integer multiple M of half the lattice coordination number. The equal time quantum correlations in their ground states may be computed as finite temperature correlations of a classical O(3) model on the same lattice, where the temperature is given by T=1/M. In dimensions d=1 and d=2 this mapping implies that all AKLT states are quantum disordered. We consider AKLT states in d=3 where the nature of the AKLT states is now a question of detail depending upon the choice of lattice and spin; for sufficiently large S some form of Neel order is almost inevitable. On the unfrustrated cubic lattice, we find that all AKLT states are ordered while for the unfrustrated diamond lattice the minimal S=2 state is disordered while all other states are ordered. On the frustrated pyrochlore lattice, we find (conservatively) that several states starting with the minimal S=3 state are disordered. The disordered AKLT models we report here are a significant addition to the catalog of magnetic Hamiltonians in d=3 with ground states known to lack order on account of strong quantum fluctuations.Comment: 7 pages, 2 figure

Mapping Extragalactic Dark Matter Annihilation with Galaxy Surveys: A Systematic Study of Stacked Group Searches

Dark matter in the halos surrounding galaxy groups and clusters can annihilate to high-energy photons. Recent advancements in the construction of galaxy group catalogs provide many thousands of potential extragalactic targets for dark matter. In this paper, we outline a procedure to infer the dark matter signal associated with a given galaxy group. Applying this procedure to a catalog of sources, one can create a full-sky map of the brightest extragalactic dark matter targets in the nearby Universe ($z\lesssim 0.03$), supplementing sources of dark matter annihilation from within the Local Group. As with searches for dark matter in dwarf galaxies, these extragalactic targets can be stacked together to enhance the signals associated with dark matter. We validate this procedure on mock $\textit{Fermi}$ gamma-ray data sets using a galaxy catalog constructed from the $\texttt{DarkSky}$ $N$-body cosmological simulation and demonstrate that the limits are robust, at $\mathcal{O}(1)$ levels, to systematic uncertainties on halo mass and concentration. We also quantify other sources of systematic uncertainty arising from the analysis and modeling assumptions. Our results suggest that a stacking analysis using galaxy group catalogs provides a powerful opportunity to discover extragalactic dark matter and complements existing studies of Milky Way dwarf galaxies.Comment: 17+7 pages, 9+4 figures; v2, updated to PRD version with several updates, results and conclusions unchange

Embedding Knowledge Graph of Patent Metadata to Measure Knowledge Proximity

Knowledge proximity refers to the strength of association between any two entities in a structural form that embodies certain aspects of a knowledge base. In this work, we operationalize knowledge proximity within the context of the US Patent Database (knowledge base) using a knowledge graph (structural form) named PatNet built using patent metadata, including citations, inventors, assignees, and domain classifications. We train various graph embedding models using PatNet to obtain the embeddings of entities and relations. The cosine similarity between the corresponding (or transformed) embeddings of entities denotes the knowledge proximity between these. We compare the embedding models in terms of their performances in predicting target entities and explaining domain expansion profiles of inventors and assignees. We then apply the embeddings of the best-preferred model to associate homogeneous (e.g., patent-patent) and heterogeneous (e.g., inventor-assignee) pairs of entities

Delamination of ceramic top coat accelerated by CMAS in an EB-PVD thermal barrier coating specimen

Application of thermal barrier coatings (TBCs) which provides thermal insulation to the underlying Nickel-based superalloy substrate has been key technologies in advanced gas turbines. More recently, it has been recognized that the TBCs can be damaged by calcium–magnesium–alumino-silicates (CMAS) resulting from siliceous minerals (dust, sand, ash) containing the intake air and from unclean fuels such as a syngas and biomass gas. In this work basic mechanisms and mechanics as well as the kinetics, were explored, via a model CMAS, by specifying a TBC specimen which consisted of a Ni-base superalloy, MCrAlY bond coat and YSZ top coat fabricated by electron beam physical vapor deposition (EB-PVD) process. It was demonstrated that the penetration and the resultant phase transformation of the YSZ with the CMAS were basic mechanisms(Fig.1(a)). It was a particular finding that the thickness of thermal grown oxide was significantly accelerated by CMAS at the top/bond coat interface, resulting in a predominant delamination of top coat(Fig.1(b)). The behavior was discussed, in comparison with that in the TBC specimen fabricated by an air plasma spraying process(Fig.1(c)). Please click Additional Files below to see the full abstract

A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions

Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two categories: those that specify a goal condition or target state, and those that specify explicit actions, or how to perform a given task. Recent approaches have used reward functions as a semantic representation of goal-based commands, which allows for the use of a state-of-the-art planner to find a policy for the given task. However, these reward functions cannot be directly used to represent action-oriented commands. We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding Network (DRAGGN), for task grounding and execution that handles natural language from either category as input, and generalizes to unseen environments. Our robot-simulation results demonstrate that a system successfully interpreting both goal-oriented and action-oriented task specifications brings us closer to robust natural language understanding for human-robot interaction.Comment: Accepted at the 1st Workshop on Language Grounding for Robotics at ACL 201